In the lava flow mitigation context, the determination of areas exposed to volcanic risk is crucial for diminishing consequences in terms of human causalities and damages of material properties. In order to mitigate the destructive effects of lava flows along volcanic slopes, the building and positioning of artificial barriers is fundamental for controlling and slowing down the lava flow advance. In this article, an evolutionary computation-based decision support system for defining and optimizing volcanic hazard mitigation interventions is proposed. In particular, the SCIARA-fv2 Cellular Automata numerical model has been applied for simulating lava flows at Mt. Etna (Italy) volcano and Parallel Genetic Algorithms (PGA) adopted for optimizing protective measures construction by morphological evolution. The PGA application regarded the optimization of the position, orientation, and extension of earth barriers built to protect Rifugio Sapienza, a touristic facility located near the summit of the volcano. A preliminary release of the algorithm, called single barrier (SBA) approach, was initially considered. Subsequently, a second GA strategy, called Evolutionary Greedy Strategy (EGS), was implemented by introducing multibarrier protection measures in order to improve the efficiency of the final solution. Finally, a Coevolutionary Cooperative Strategy (CCS), has been introduced where all barriers are encoded in the genotype and, because all the constituents parts of the solution interact with the GA environment, a mechanism of cooperation between individuals has been favored. The study has produced extremely positive results and represents, to our knowledge, the first application of morphological evolution for lava flow mitigation.
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|Titolo:||Morphological Coevolution for Fluid Dynamical-Related Risk Mitigation|
|Data di pubblicazione:||2016|
|Appare nelle tipologie:||1.1 Articolo in rivista|